[leftmargin=3mm]
SchNet [1]} is a energy-centric GNN that uses scalable atom-pair-based message passing; hence, a relatively large model size (5-layer message passing with 1024-dimensional hidden channels) can be trained with 194 GPU hours, which is comparable to ForceNet-large.


DimeNet++ [2]} is a recent improvement of DimeNet [3]} and is also an energy-centric GNN. It uses atom-triplet-based message passing to capture angular information, which makes it computationally expensive.
Even training DimeNet++ of a relatively-small model size (3-layer message passing with 192-dimensional hidden channels) requires 587 GPU days—18.9 and 3.0 times more expensive than ForceNet and ForceNet-large, respectively.
Training DimeNet++-large (3-layer message-passing with 512-dimensional hidden channels) takes a significant 1600 GPU days of compute, being 51.6 and 8.2 times more expensive than ForceNet and ForceNet-large, respectively.


GNS model [4]} is a scalable force-centric model that directly predicts atomic forces.
We make its model size (in terms of the number of parameters) comparable to ForceNet. The training takes 20 GPU days, which is \(1.6\times \)  faster than ForceNet. However, as we will see, the performance of ForceNet is better even if ForceNet's training is truncated at 20 GPU days.

